Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 49

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fb984ca4240>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 9

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fb984bb6c50>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.3.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function

    real_input = tf.placeholder(tf.float32,(None,image_width,image_height,image_channels),name='input_real')
    z_input = tf.placeholder(tf.float32,(None,z_dim),name='input_z')
    learning_rate = tf.placeholder(tf.float32)
    return (real_input,z_input,learning_rate)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/opt/conda/lib/python3.6/runpy.py", line 193, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/opt/conda/lib/python3.6/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>\n    app.launch_new_instance()', 'File "/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 478, in start\n    self.io_loop.start()', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/ioloop.py", line 177, in start\n    super(ZMQIOLoop, self).start()', 'File "/opt/conda/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events\n    self._handle_recv()', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback\n    callback(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 281, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 232, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 397, in execute_request\n    user_expressions, allow_stdin)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2728, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2856, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2910, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-5-6c389b426afd>", line 23, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/home/workspace/face_generation/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/home/workspace/face_generation/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/home/workspace/face_generation/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/home/workspace/face_generation/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 175, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 144, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 101, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = .01
    with tf.variable_scope('discriminator',reuse=reuse):
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, kernel_initializer=tf.contrib.layers.xavier_initializer(), padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        # 14x14x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, kernel_initializer=tf.contrib.layers.xavier_initializer(), padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 7x7x128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, kernel_initializer=tf.contrib.layers.xavier_initializer(), padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        # 4x4x256
        
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return(out,logits)
        

    


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
/opt/conda/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    reuse = not is_train
    
    with tf.variable_scope('generator',reuse=reuse):
        alpha=.2
         # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 256))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 7x7x512 now
        
        x2 = tf.layers.conv2d_transpose(x1, 128, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 14x14x256 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x2,out_channel_dim,5, strides=2, padding='same')
       
        
        out = tf.tanh(logits)
        
        return(out)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    smooth = .1
    g_model = generator(input_z,out_channel_dim)
    d_model_real,d_logits_real = discriminator(input_real,reuse=False)
    d_model_fake,d_logits_fake = discriminator(g_model,reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_real,
                                                                        labels = tf.ones_like(
                                                                        d_model_real)*(1-smooth)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake,
                                                                        labels = tf.zeros_like(
                                                                        d_model_fake)))
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake,
                                                                   labels = tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    
    return d_loss,g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_variables = tf.trainable_variables()
    d_variables = [var for var in t_variables if var.name.startswith('discriminator')]
    g_variables = [var for var in t_variables if var.name.startswith('generator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_optimiser = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(d_loss,var_list=d_variables)
        g_train_optimiser = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(g_loss,var_list=g_variables)
    
    return d_train_optimiser,g_train_optimiser


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    width,height,channel = data_shape[1],data_shape[2],data_shape[3]
    
    real_input,z_input,learn_rate = model_inputs(width,height,channel,z_dim)
    
    d_loss,g_loss = model_loss(real_input,z_input,channel)
    
    d_opt,g_opt = model_opt(d_loss,g_loss,learning_rate,beta1)
    
    saver = tf.train.Saver()

    steps = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                batch_images *= 2
                steps += 1
                
                #sample random nosie for G
                batch_z = np.random.uniform(-1,1,size=(batch_size,z_dim))
                
                #optimiser running
                _ = sess.run(d_opt,feed_dict={real_input : batch_images,
                                             z_input : batch_z,
                                             learn_rate : learning_rate})
                _ = sess.run(g_opt,feed_dict={z_input : batch_z,
                                             real_input : batch_images,
                                             learn_rate : learning_rate})
                if steps%100 ==0:
                    train_loss_d = d_loss.eval({z_input: batch_z,real_input:batch_images})
                    train_loss_g = g_loss.eval({z_input: batch_z})
                    
                    print("Epoch {}/{}..., step {}".format(epoch_i+1, epochs, steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                if steps % 100 == 0:
                    show_generator_output(sess, 16, z_input, channel, data_image_mode)
                    
                
        saver.save(sess, './generator.ckpt')

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [56]:
batch_size = 32
z_dim = 128
learning_rate = .0002
beta1 = .5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2..., step 100 Discriminator Loss: 0.8093... Generator Loss: 1.2343
Epoch 1/2..., step 200 Discriminator Loss: 0.3923... Generator Loss: 3.2283
Epoch 1/2..., step 300 Discriminator Loss: 0.3509... Generator Loss: 4.6910
Epoch 1/2..., step 400 Discriminator Loss: 0.3966... Generator Loss: 3.4114
Epoch 1/2..., step 500 Discriminator Loss: 0.4126... Generator Loss: 3.7196
Epoch 1/2..., step 600 Discriminator Loss: 0.8455... Generator Loss: 1.2038
Epoch 1/2..., step 700 Discriminator Loss: 0.3546... Generator Loss: 4.6381
Epoch 1/2..., step 800 Discriminator Loss: 0.4100... Generator Loss: 2.7953
Epoch 1/2..., step 900 Discriminator Loss: 0.5788... Generator Loss: 2.0214
Epoch 1/2..., step 1000 Discriminator Loss: 0.5088... Generator Loss: 2.1017
Epoch 1/2..., step 1100 Discriminator Loss: 0.3821... Generator Loss: 3.8602
Epoch 1/2..., step 1200 Discriminator Loss: 0.5551... Generator Loss: 2.9413
Epoch 1/2..., step 1300 Discriminator Loss: 0.4698... Generator Loss: 2.8564
Epoch 1/2..., step 1400 Discriminator Loss: 0.4101... Generator Loss: 2.7700
Epoch 1/2..., step 1500 Discriminator Loss: 0.3739... Generator Loss: 3.5081
Epoch 1/2..., step 1600 Discriminator Loss: 0.7154... Generator Loss: 1.4182
Epoch 1/2..., step 1700 Discriminator Loss: 0.3590... Generator Loss: 4.1432
Epoch 1/2..., step 1800 Discriminator Loss: 0.4920... Generator Loss: 2.4398
Epoch 2/2..., step 1900 Discriminator Loss: 0.4095... Generator Loss: 2.8758
Epoch 2/2..., step 2000 Discriminator Loss: 0.4179... Generator Loss: 2.8010
Epoch 2/2..., step 2100 Discriminator Loss: 0.3733... Generator Loss: 3.9676
Epoch 2/2..., step 2200 Discriminator Loss: 0.3960... Generator Loss: 3.0665
Epoch 2/2..., step 2300 Discriminator Loss: 0.4190... Generator Loss: 2.7612
Epoch 2/2..., step 2400 Discriminator Loss: 0.4911... Generator Loss: 2.1400
Epoch 2/2..., step 2500 Discriminator Loss: 0.4439... Generator Loss: 2.5114
Epoch 2/2..., step 2600 Discriminator Loss: 0.4170... Generator Loss: 2.8131
Epoch 2/2..., step 2700 Discriminator Loss: 0.3849... Generator Loss: 3.1789
Epoch 2/2..., step 2800 Discriminator Loss: 0.3854... Generator Loss: 3.1695
Epoch 2/2..., step 2900 Discriminator Loss: 0.3949... Generator Loss: 3.3823
Epoch 2/2..., step 3000 Discriminator Loss: 0.5555... Generator Loss: 1.8594
Epoch 2/2..., step 3100 Discriminator Loss: 0.4380... Generator Loss: 2.4933
Epoch 2/2..., step 3200 Discriminator Loss: 0.4044... Generator Loss: 2.8018
Epoch 2/2..., step 3300 Discriminator Loss: 0.5814... Generator Loss: 1.7025
Epoch 2/2..., step 3400 Discriminator Loss: 0.3848... Generator Loss: 3.1669
Epoch 2/2..., step 3500 Discriminator Loss: 0.3570... Generator Loss: 4.3890
Epoch 2/2..., step 3600 Discriminator Loss: 0.4184... Generator Loss: 2.7051
Epoch 2/2..., step 3700 Discriminator Loss: 0.3980... Generator Loss: 3.0788

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [12]:
batch_size = 32
z_dim = 120
learning_rate = .0004
beta1 = .3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1..., step 100 Discriminator Loss: 1.1024... Generator Loss: 2.5615
Epoch 1/1..., step 200 Discriminator Loss: 1.6290... Generator Loss: 0.4328
Epoch 1/1..., step 300 Discriminator Loss: 1.6332... Generator Loss: 0.3639
Epoch 1/1..., step 400 Discriminator Loss: 1.3112... Generator Loss: 0.7693
Epoch 1/1..., step 500 Discriminator Loss: 1.6136... Generator Loss: 0.3972
Epoch 1/1..., step 600 Discriminator Loss: 1.4380... Generator Loss: 0.4798
Epoch 1/1..., step 700 Discriminator Loss: 1.1821... Generator Loss: 0.6217
Epoch 1/1..., step 800 Discriminator Loss: 1.5626... Generator Loss: 0.3908
Epoch 1/1..., step 900 Discriminator Loss: 1.3516... Generator Loss: 0.5160
Epoch 1/1..., step 1000 Discriminator Loss: 0.7281... Generator Loss: 1.5811
Epoch 1/1..., step 1100 Discriminator Loss: 1.6771... Generator Loss: 0.3825
Epoch 1/1..., step 1200 Discriminator Loss: 1.3130... Generator Loss: 0.5336
Epoch 1/1..., step 1300 Discriminator Loss: 1.5588... Generator Loss: 0.4939
Epoch 1/1..., step 1400 Discriminator Loss: 0.4951... Generator Loss: 2.1147
Epoch 1/1..., step 1500 Discriminator Loss: 0.5819... Generator Loss: 2.2015
Epoch 1/1..., step 1600 Discriminator Loss: 0.4836... Generator Loss: 2.2848
Epoch 1/1..., step 1700 Discriminator Loss: 0.4349... Generator Loss: 4.6743
Epoch 1/1..., step 1800 Discriminator Loss: 1.7263... Generator Loss: 3.8304
Epoch 1/1..., step 1900 Discriminator Loss: 1.2244... Generator Loss: 2.1640
Epoch 1/1..., step 2000 Discriminator Loss: 0.8130... Generator Loss: 1.5750
Epoch 1/1..., step 2100 Discriminator Loss: 0.7095... Generator Loss: 1.6828
Epoch 1/1..., step 2200 Discriminator Loss: 0.7281... Generator Loss: 1.9452
Epoch 1/1..., step 2300 Discriminator Loss: 0.8836... Generator Loss: 1.5011
Epoch 1/1..., step 2400 Discriminator Loss: 0.4392... Generator Loss: 3.2592
Epoch 1/1..., step 2500 Discriminator Loss: 0.5915... Generator Loss: 4.1966
Epoch 1/1..., step 2600 Discriminator Loss: 0.5712... Generator Loss: 2.1571
Epoch 1/1..., step 2700 Discriminator Loss: 0.5318... Generator Loss: 3.0417
Epoch 1/1..., step 2800 Discriminator Loss: 1.4261... Generator Loss: 0.4807
Epoch 1/1..., step 2900 Discriminator Loss: 0.6232... Generator Loss: 1.6033
Epoch 1/1..., step 3000 Discriminator Loss: 0.4528... Generator Loss: 2.6072
Epoch 1/1..., step 3100 Discriminator Loss: 2.2437... Generator Loss: 0.2070
Epoch 1/1..., step 3200 Discriminator Loss: 1.4222... Generator Loss: 0.5074
Epoch 1/1..., step 3300 Discriminator Loss: 0.4230... Generator Loss: 2.9257
Epoch 1/1..., step 3400 Discriminator Loss: 0.5934... Generator Loss: 2.0658
Epoch 1/1..., step 3500 Discriminator Loss: 1.6908... Generator Loss: 3.8477
Epoch 1/1..., step 3600 Discriminator Loss: 0.6366... Generator Loss: 1.8825
Epoch 1/1..., step 3700 Discriminator Loss: 0.7033... Generator Loss: 1.4331
Epoch 1/1..., step 3800 Discriminator Loss: 1.4116... Generator Loss: 0.6375
Epoch 1/1..., step 3900 Discriminator Loss: 0.6024... Generator Loss: 4.0866
Epoch 1/1..., step 4000 Discriminator Loss: 1.7385... Generator Loss: 0.4232
Epoch 1/1..., step 4100 Discriminator Loss: 0.7797... Generator Loss: 2.1448
Epoch 1/1..., step 4200 Discriminator Loss: 0.6554... Generator Loss: 1.4972
Epoch 1/1..., step 4300 Discriminator Loss: 0.5411... Generator Loss: 2.2526
Epoch 1/1..., step 4400 Discriminator Loss: 1.3703... Generator Loss: 2.6697
Epoch 1/1..., step 4500 Discriminator Loss: 0.8979... Generator Loss: 2.0125
Epoch 1/1..., step 4600 Discriminator Loss: 0.4091... Generator Loss: 3.1993
Epoch 1/1..., step 4700 Discriminator Loss: 0.7462... Generator Loss: 1.3706
Epoch 1/1..., step 4800 Discriminator Loss: 1.5716... Generator Loss: 0.5310
Epoch 1/1..., step 4900 Discriminator Loss: 0.7846... Generator Loss: 1.2703
Epoch 1/1..., step 5000 Discriminator Loss: 0.5092... Generator Loss: 2.0916
Epoch 1/1..., step 5100 Discriminator Loss: 0.4950... Generator Loss: 2.2712
Epoch 1/1..., step 5200 Discriminator Loss: 0.7339... Generator Loss: 1.4010
Epoch 1/1..., step 5300 Discriminator Loss: 0.5700... Generator Loss: 1.8825
Epoch 1/1..., step 5400 Discriminator Loss: 0.6380... Generator Loss: 2.0118
Epoch 1/1..., step 5500 Discriminator Loss: 1.1528... Generator Loss: 0.7453
Epoch 1/1..., step 5600 Discriminator Loss: 0.5146... Generator Loss: 2.0643
Epoch 1/1..., step 5700 Discriminator Loss: 0.4118... Generator Loss: 4.0631
Epoch 1/1..., step 5800 Discriminator Loss: 0.4869... Generator Loss: 2.1636
Epoch 1/1..., step 5900 Discriminator Loss: 0.4950... Generator Loss: 2.2151
Epoch 1/1..., step 6000 Discriminator Loss: 1.2281... Generator Loss: 0.7331
Epoch 1/1..., step 6100 Discriminator Loss: 1.3984... Generator Loss: 2.8794
Epoch 1/1..., step 6200 Discriminator Loss: 2.0436... Generator Loss: 0.3061
Epoch 1/1..., step 6300 Discriminator Loss: 0.5763... Generator Loss: 2.1014

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.